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Found 9 result(s)
The Biological and Chemical Oceanography Data Management Office (BCO-DMO) is a publicly accessible earth science data repository created to curate, publicly serve (publish), and archive digital data and information from biological, chemical and biogeochemical research conducted in coastal, marine, great lakes and laboratory environments. The BCO-DMO repository works closely with investigators funded through the NSF OCE Division’s Biological and Chemical Sections and the Division of Polar Programs Antarctic Organisms & Ecosystems. The office provides services that span the full data life cycle, from data management planning support and DOI creation, to archive with appropriate national facilities.
Atmosphere to Electrons (A2e) is a new, multi-year, multi-stakeholder U.S. Department of Energy (DOE) research and development initiative tasked with improving wind plant performance and mitigating risk and uncertainty to achieve substantial reduction in the cost of wind energy production. The A2e strategic vision will enable a new generation of wind plant technology, in which smart wind plants are designed to achieve optimized performance stemming from more complete knowledge of the inflow wind resource and complex flow through the wind plant.
The Community Coordinated Modeling Center (CCMC) is a multi-agency partnership based at the NASA Goddard Space Flight Center in Greenbelt, Maryland and a component of the National Space Weather Program. The CCMC provides, to the international research community, access to modern space science simulations. In addition, the CCMC supports the transition to space weather operations of modern space research models.
Funded by the National Science Foundation (NSF) and proudly operated by Battelle, the National Ecological Observatory Network (NEON) program provides open, continental-scale data across the United States that characterize and quantify complex, rapidly changing ecological processes. The Observatory’s comprehensive design supports greater understanding of ecological change and enables forecasting of future ecological conditions. NEON collects and processes data from field sites located across the continental U.S., Puerto Rico, and Hawaii over a 30-year timeframe. NEON provides free and open data that characterize plants, animals, soil, nutrients, freshwater, and the atmosphere. These data may be combined with external datasets or data collected by individual researchers to support the study of continental-scale ecological change.
The Arctic Data Center is the primary data and software repository for the Arctic section of NSF Polar Programs. The Center helps the research community to reproducibly preserve and discover all products of NSF-funded research in the Arctic, including data, metadata, software, documents, and provenance that links these together. The repository is open to contributions from NSF Arctic investigators, and data are released under an open license (CC-BY, CC0, depending on the choice of the contributor). All science, engineering, and education research supported by the NSF Arctic research program are included, such as Natural Sciences (Geoscience, Earth Science, Oceanography, Ecology, Atmospheric Science, Biology, etc.) and Social Sciences (Archeology, Anthropology, Social Science, etc.). Key to the initiative is the partnership between NCEAS at UC Santa Barbara, DataONE, and NOAA’s NCEI, each of which bring critical capabilities to the Center. Infrastructure from the successful NSF-sponsored DataONE federation of data repositories enables data replication to NCEI, providing both offsite and institutional diversity that are critical to long term preservation.
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Finnish Meteorological Institute (FMI) research data repository METIS is provided by EUDAT and enables the institute data to be preserved, discovered, and accessed. FMI covers a wide range of research on weather, sea, climate and space. According to the FMI's Research Data policy , publicly funded research data must be made available to the widest possible audience (under CC BY license, at the minimum), as the best way to maximize the data impact but also to do justice to all the hard labor put into collecting, cleaning, and analyzing the data.